Data-driven health care

Five stories of how organizations are tackling global health issues with big data analytics

The emergence of data-driven health care has presented tremendous opportunities as well as unprecedented challenges. Reducing health care costs while improving quality of care, handling complex, ever-changing demographics, and adapting to the rapid increase and globalization of patient data are just a few of the realities facing health care providers, insurers, pharmaceutical companies and government entities today.

The need to derive insights from trusted health data has never been greater. The following stories illustrate just a few ways organizations today are using analytics to harness the potential of data, gain new insights, and transform the way we look at health at the individual and global level.

A new era of medical research

For years doctors and hospitals around the world have collected information on various diseases, their diagnoses and treatments for research. Most of this information is stored within individual databases, but since 2009 BioGrid Australia has been providing the unique ability to link patient information from these widespread databases – all in a way that maintains the privacy of the patient and the security of the information.

Information from greater numbers of patients can be combined, now globally, leading to more effective and powerful research. Researchers can drag and drop their data onto a screen in a mobile environment… having access to something at their fingertips on a mobile device will make things much easier.

Following the introduction of SAS Visual Analytics, BioGrid has also been able to transform the accessibility and speed at which researchers can access patient data. The data visualization software allows them to see and explore more data than ever before.

“Information from greater numbers of patients can be combined, now globally, leading to more effective and powerful research,” says Maureen Turner, BioGrid’s CEO. “Researchers can drag and drop their data onto a screen in a mobile environment. They might be at their computer, but having access to something at their fingertips on a mobile device will make things much easier.”

Professor and renowned genomic researcher Finlay Macrae adds: “It means that we’re not just dependent on our own experience. We’re not just dependent on Australian experience. We can actually draw on the whole world’s experience to understand, for example, what a particular variation in DNA means for a particular family.”

Comparing health cases in real time

New Zealand health care providers and their patients are able to benefit from cloud-based data visualization. HealthStat, a research tool created by CBG Health Research and powered by SAS Visual Analytics, enables primary health organizations to identify trends in real time, such as flu or gastroenteritis outbreaks. In turn, individual practices can compare their cases with others around the country to improve treatment effectiveness.

“Gaps in health care can be identified and dealt with faster than ever before,” says Barry Gribben, CBG founder and Medical Director. “For example, if there is a flu outbreak, PHOs can monitor how many patients are being treated by particular hospitals and what treatment they are being offered.”

It also means that individual practices can see at a glance – instead of going through pages of reports and data, as was done in the past – which of their patients are due for a treatment or follow-ups.

“Many of the analyses we produce are publishable,” says Gribben. “A traditional research organization might do a study based on collecting and analyzing data from 500 doctors, requiring significant funding. We can do these analyses in half an hour, producing real results that can change policy.”

Collaborative, data-driven policymaking

Along with CBG in New Zealand, Bupa Health Dialog in Australia is sharing its member insights to inform policy and programs. The company’s population health analytics uncovers existing and emerging health patterns and determines future demands on the health system.

At the patient level, Bupa Health Dialog uses SAS to analyze individual health data within targeted populations. It looks at factors such as age, chronic condition lifestyle risk factors, previous hospital admissions and financial risk to provide a comprehensive view of the health and wellness of that target population and identifies the prevalence of chronic disease and risk factors. Through these analyses, Bupa Health Dialog is able to identify quality intervention methods to support members of its health network.

One example is with the Australian Dept. of Veterans’ Affairs. Its health care data is analyzed to proactively identify veterans who have chronic health conditions, are most at risk of hospitalization, and would benefit from an enhanced level of health care and management. By identifying this at-risk population, Bupa Health Dialog can work with the veterans’ general practitioners to work toward improved, coordinated and community-based health care and thus reduce the need for hospital stays.

Slashing claims leakage

As Bupa continues to innovate in population health analytics, it is also making headway in reducing claims leakage. An all-too-familiar challenge for private and public sector health insurers, claims leakage covers everything from overpayment of claims and accidental user error to submission of false claims and ineffective system controls. And with health claim digitization, this form of risk becomes even more fast-paced and likely to change.

“It is almost impossible to identify how big a claims leakage problem is,” says Michael Douman, Head of Business and Clinical Analysis at Bupa Australia. His team has annual savings targets in this area – $22 million in 2014 alone – but with the help of SAS, they have regularly exceeded their target year after year.

The best way of identifying where there are issues, Douman says, is to use the data to highlight where system controls aren’t working. For example, you could look at all cases where a patient has received more than one implantable cardiac defibrillator within the last three years. “When we find such cases, we know it’s incorrect because defibrillators are covered by a manufacturer’s warranty. And with defibrillators costing around $65,000 each, this is worth reviewing.”

Bupa has also been able to identify cases where hospitals, clinicians and ancillary providers enter the wrong code for treatment, which can result in higher claim payments. “An associated issue may involve hospital clinicians not completing patient discharge summaries. When this happens, it means hospital clinical coders make assumptions about the treatment and the associated coding on which the hospital invoice is based,” Douman says. “If the assumption is wrong, we can end up overpaying. SAS is invaluable in identifying these situations.”

While claims leakage is difficult to prove, there are deterrents to prevent it from reoccurring, he says. This includes seeking the recovery of overpaid funds and ensuring Bupa’s preferred provider networks maintain integrity and have the right systems controls in place.

Saving lives by studying data

On the other side of the world, in Wake County, North Carolina, heart attack victims have a better chance at survival thanks to new, analytics-driven recommendations from the county’s Emergency Medical Services (EMS). Based on a data analysis project conducted by the SAS Advanced Analytics Lab for State and Local Government, Wake County EMS changed its recommendations for how long to conduct CPR on cardiac arrest patients.

For years, emergency responders administered CPR to heart attack victims for up to 25 minutes, which was the industry standard. Wake County EMS personnel believed, based on anecdotal experience, that patients could recover fully if CPR was continued far beyond that time.

Wake County EMS responds to approximately 88,000 calls per year, including 500 cardiac arrests that require CPR. Wake County EMS had 20 years of data about cardiac arrest patients and whether EMS crews restored a pulse doing CPR, but lacked information on how the patients fared once they were in the hospital.

In 2005, Wake County EMS began to receive data from area hospitals on patient outcomes. It hired SAS to analyze data on all responses to cardiac arrest calls from 2005 through 2012, including the amount of time spent administering CPR, and the patient outcomes.

As a result of the analyses, Wake County EMS has changed its recommendations for cardiac patients. If a patient has a flatline on a heart monitor, emergency responders may stop CPR after 25-30 minutes. However, if they are able to see cardiac activity during CPR, they may continue CPR for up to an hour or more without worrying that the patient will have a higher chance of ending up in a vegetative state.

Wake County EMS found that if they had continued under the old guideline from 2005-2012 and ceased CPR at 25 minutes, 100 people (who ultimately left the hospital) would have died.

“By practicing evidence-based medicine, guided by data, many Wake County residents are alive today who wouldn’t have been,” said Dr. Brent Myers, Director of Wake County EMS. “Our recognition at the annual meeting gives us hope that our approach will be replicated by other EMS groups around the country, and save more lives.”

Wake County EMS plans additional projects with the SAS Advanced Analytics Lab that could improve care and efficiencies. For instance, it plans to look for specific predictors of survival with intact brain function during extended-duration CPR. It will investigate whether the amount of carbon dioxide exhaled by a patient can help guide EMS crews’ decision making during cardiac arrest resuscitations.